There are two main unresolved issues with regard to the application of CNNs for medical image segmentation. The first issue has to do with the training procedures and training data. Specifically, the number of manually-labeled images that are available for training is typically very small compared with many non-medical applications. This is because the number of images is small to begin with, and accurate manual annotation is costly because it depends on domain expertise. In recent years, this challenge has led to a surge of interest in such techniques as transfer learning [1]}, unsupervised learning [2]}, and learning from inaccurate and computer-generated annotations [3]}.
